There is a clear need for a robust method to image cerebral hemodynamics to better understand disease processes, as well as to develop early stage disease biomarkers. Many diseases like cancer and stroke are characterized by deficits in perfusion and changes in other blood flow related parameters. There is also mounting evidence suggesting that poor regulation of cerebral blood flow is closely linked to the development of several diseases whose prevalence is on the rise, like multiple sclerosis and Alzheimer's disease. While routine clinical perfusion imaging is cumbersome and requires the injection of contrast agents. Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique to image perfusion without injection of contrast agents. ASL is rapidly gaining prominence in the clinic because perfusion can yield crucial information about the health of brain tissue and its vasculature, although it is being used with increasing frequency in other organs as well. While ASL's potential as a clinical and research tool is phenomenal, the technique is still severely challenged by low signal to noise ratio (SNR). These challenges are exacerbated in the white matter because of its low perfusion rate and longer transit time from the labeling site to the voxel. In this proposal we aim to develop a new acquisition and quantification framework for imaging perfusion and other physiologically relevant parameters. This framework takes advantage of newly developed MR fingerprinting techniques. We aim to optimize this framework in order to maximize the sensitivity and accuracy, while reducing the scan time. Finally, we aim to validate the technique by comparison to independent measurements of the same parameters using alternative, established methods.